IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-12817-y.html
   My bibliography  Save this article

Overcoming resolution limits with quantum sensing

Author

Listed:
  • T. Gefen

    (The Hebrew University of Jerusalem)

  • A. Rotem

    (The Hebrew University of Jerusalem)

  • A. Retzker

    (The Hebrew University of Jerusalem)

Abstract

The field of quantum sensing explores the use of quantum phenomena to measure a broad range of physical quantities, of both static and time-dependent types. While for static signals the main figure of merit is sensitivity, for time dependent signals it is spectral resolution, i.e. the ability to resolve two different frequencies. Here we study this problem, and develop new superresolution methods that rely on quantum features. We first formulate a general criterion for superresolution in quantum problems. Inspired by this, we show that quantum detectors can resolve two frequencies from incoherent segments of the signal, irrespective of their separation, in contrast to what is known about classical detection schemes. The main idea behind these methods is to overcome the vanishing distinguishability in resolution problems by nullifying the projection noise.

Suggested Citation

  • T. Gefen & A. Rotem & A. Retzker, 2019. "Overcoming resolution limits with quantum sensing," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12817-y
    DOI: 10.1038/s41467-019-12817-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-12817-y
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-019-12817-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ozgur Sahin & Erica Leon Sanchez & Sophie Conti & Amala Akkiraju & Paul Reshetikhin & Emanuel Druga & Aakriti Aggarwal & Benjamin Gilbert & Sunil Bhave & Ashok Ajoy, 2022. "High field magnetometry with hyperpolarized nuclear spins," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Mateusz Mazelanik & Adam Leszczyński & Michał Parniak, 2022. "Optical-domain spectral super-resolution via a quantum-memory-based time-frequency processor," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12817-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.